OpenRaiser/Intern-Atlas huggingface dataset:
Segment anything, from space?
Venue: arXiv cs.CV 2023
Year: 2023

Recently, the first foundation model developed specifically for image segmentation tasks was developed, termed the "Segment Anything Model" (SAM). SAM can segment objects in input imagery based on cheap input prompts, such as one (or more) points, a bounding box, or a mask. The authors examined the \textit{zero-shot} image segmentation accuracy of SAM on a large number of vision benchmark tasks and found that SAM usually achieved recognition accuracy similar to, or sometimes exceeding, vision models that had been trained on the target tasks. The impressive generalization of SAM for segmentation has major implications for vision researchers working on natural imagery. In this work, we examine whether SAM's performance extends to overhead imagery problems and help guide the community's response to its development. We examine SAM's performance on a set of diverse and widely studied benchmark tasks. We find that SAM does often generalize well to overhead imagery, although it fails in some cases due to the unique characteristics of overhead imagery and its common target objects. We report on these unique systematic failure cases for remote sensing imagery that may comprise useful future research for the community.
PoseVocab: Learning Joint-structured Pose Embeddings for Human Avatar Modeling
Venue: arXiv cs.CV 2023
Year: 2023

Creating pose-driven human avatars is about modeling the mapping from the low-frequency driving pose to high-frequency dynamic human appearances, so an effective pose encoding method that can encode high-fidelity human details is essential to human avatar modeling. To this end, we present PoseVocab, a novel pose encoding method that encourages the network to discover the optimal pose embeddings for learning the dynamic human appearance. Given multi-view RGB videos of a character, PoseVocab constructs key poses and latent embeddings based on the training poses. To achieve pose generalization and temporal consistency, we sample key rotations in $so(3)$ of each joint rather than the global pose vectors, and assign a pose embedding to each sampled key rotation. These joint-structured pose embeddings not only encode the dynamic appearances under different key poses, but also factorize the global pose embedding into joint-structured ones to better learn the appearance variation related to the motion of each joint. To improve the representation ability of the pose embedding while maintaining memory efficiency, we introduce feature lines, a compact yet effective 3D representation, to model more fine-grained details of human appearances. Furthermore, given a query pose and a spatial position, a hierarchical query strategy is introduced to interpolate pose embeddings and acquire the conditional pose feature for dynamic human synthesis. Overall, PoseVocab effectively encodes the dynamic details of human appearance and enables realistic and generalized animation under novel poses. Experiments show that our method outperforms other state-of-the-art baselines both qualitatively and quantitatively in terms of synthesis quality. Code is available at https://github.com/lizhe00/PoseVocab.
Parameterized Verification of Coverability in Infinite State Broadcast Networks
Venue: arXiv cs.LO 2023
Year: 2023

Parameterized verification of coverability in broadcast networks with finite state processes has been studied for different types of models and topologies. In this paper, we attempt to develop a theory of broadcast networks in which the processes can be well-structured transition systems. The resulting formalism is called well-structured broadcast networks. For various types of communication topologies, we prove the decidability of coverability in the static case, i.e, when the network topology is not allowed to change. We do this by showing that for these types of static communication topologies, the broadcast network itself is a well-structured transition system, hence proving the decidability of coverability in the broadcast network. We also give an algorithm to decide coverability of well-structured broadcast networks when reconfiguration of links between nodes is allowed. Finally, with minor modifications of this algorithm we prove decidability of coverability when the underlying process is a pushdown automaton.
Organizational Governance of Emerging Technologies: AI Adoption in Healthcare
Venue: arXiv cs.AI 2023
Year: 2023

Private and public sector structures and norms refine how emerging technology is used in practice. In healthcare, despite a proliferation of AI adoption, the organizational governance surrounding its use and integration is often poorly understood. What the Health AI Partnership (HAIP) aims to do in this research is to better define the requirements for adequate organizational governance of AI systems in healthcare settings and support health system leaders to make more informed decisions around AI adoption. To work towards this understanding, we first identify how the standards for the AI adoption in healthcare may be designed to be used easily and efficiently. Then, we map out the precise decision points involved in the practical institutional adoption of AI technology within specific health systems. Practically, we achieve this through a multi-organizational collaboration with leaders from major health systems across the United States and key informants from related fields. Working with the consultancy IDEO [dot] org, we were able to conduct usability-testing sessions with healthcare and AI ethics professionals. Usability analysis revealed a prototype structured around mock key decision points that align with how organizational leaders approach technology adoption. Concurrently, we conducted semi-structured interviews with 89 professionals in healthcare and other relevant fields. Using a modified grounded theory approach, we were able to identify 8 key decision points and comprehensive procedures throughout the AI adoption lifecycle. This is one of the most detailed qualitative analyses to date of the current governance structures and processes involved in AI adoption by health systems in the United States. We hope these findings can inform future efforts to build capabilities to promote the safe, effective, and responsible adoption of emerging technologies in healthcare.
Real-time Autonomous Glider Navigation Software
Venue: arXiv cs.RO 2023
Year: 2023

Underwater gliders are widely utilized for ocean sampling, surveillance, and other various oceanic applications. In the context of complex ocean environments, gliders may yield poor navigation performance due to strong ocean currents, thus requiring substantial human effort during the manual piloting process. To enhance navigation accuracy, we developed a real-time autonomous glider navigation software, named GENIoS Python, which generates waypoints based on flow predictions to assist human piloting. The software is designed to closely check glider status, provide customizable experiment settings, utilize lightweight computing resources, offer stably communicate with dockservers, robustly run for extended operation time, and quantitatively compare flow estimates, which add to its value as an autonomous tool for underwater glider navigation.
ESimCSE Unsupervised Contrastive Learning Jointly with UDA Semi-Supervised Learning for Large Label System Text Classification Mode
Venue: arXiv cs.LG 2023
Year: 2023

The challenges faced by text classification with large tag systems in natural language processing tasks include multiple tag systems, uneven data distribution, and high noise. To address these problems, the ESimCSE unsupervised comparative learning and UDA semi-supervised comparative learning models are combined through the use of joint training techniques in the models.The ESimCSE model efficiently learns text vector representations using unlabeled data to achieve better classification results, while UDA is trained using unlabeled data through semi-supervised learning methods to improve the prediction performance of the models and stability, and further improve the generalization ability of the model. In addition, adversarial training techniques FGM and PGD are used in the model training process to improve the robustness and reliability of the model. The experimental results show that there is an 8% and 10% accuracy improvement relative to Baseline on the public dataset Ruesters as well as on the operational dataset, respectively, and a 15% improvement in manual validation accuracy can be achieved on the operational dataset, indicating that the method is effective.
CN-DHF: Compact Neural Double Height-Field Representations of 3D Shapes
Venue: arXiv cs.CV 2023
Year: 2023

We introduce CN-DHF (Compact Neural Double-Height-Field), a novel hybrid neural implicit 3D shape representation that is dramatically more compact than the current state of the art. Our representation leverages Double-Height-Field (DHF) geometries, defined as closed shapes bounded by a pair of oppositely oriented height-fields that share a common axis, and leverages the following key observations: DHFs can be compactly encoded as 2D neural implicits that capture the maximal and minimal heights along the DHF axis; and typical closed 3D shapes are well represented as intersections of a very small number (three or fewer) of DHFs. We represent input geometries as CNDHFs by first computing the set of DHFs whose intersection well approximates each input shape, and then encoding these DHFs via neural fields. Our approach delivers high-quality reconstructions, and reduces the reconstruction error by a factor of 2:5 on average compared to the state-of-the-art, given the same parameter count or storage capacity. Compared to the best-performing alternative, our method produced higher accuracy models on 94% of the 400 input shape and parameter count combinations tested.
Dynamic Datasets and Market Environments for Financial Reinforcement Learning
Venue: arXiv cs.LG 2023
Year: 2023

The financial market is a particularly challenging playground for deep reinforcement learning due to its unique feature of dynamic datasets. Building high-quality market environments for training financial reinforcement learning (FinRL) agents is difficult due to major factors such as the low signal-to-noise ratio of financial data, survivorship bias of historical data, and model overfitting. In this paper, we present FinRL-Meta, a data-centric and openly accessible library that processes dynamic datasets from real-world markets into gym-style market environments and has been actively maintained by the AI4Finance community. First, following a DataOps paradigm, we provide hundreds of market environments through an automatic data curation pipeline. Second, we provide homegrown examples and reproduce popular research papers as stepping stones for users to design new trading strategies. We also deploy the library on cloud platforms so that users can visualize their own results and assess the relative performance via community-wise competitions. Third, we provide dozens of Jupyter/Python demos organized into a curriculum and a documentation website to serve the rapidly growing community. The open-source codes for the data curation pipeline are available at https://github.com/AI4Finance-Foundation/FinRL-Meta
Sebis at SemEval-2023 Task 7: A Joint System for Natural Language Inference and Evidence Retrieval from Clinical Trial Reports
Venue: arXiv cs.CL 2023
Year: 2023

With the increasing number of clinical trial reports generated every day, it is becoming hard to keep up with novel discoveries that inform evidence-based healthcare recommendations. To help automate this process and assist medical experts, NLP solutions are being developed. This motivated the SemEval-2023 Task 7, where the goal was to develop an NLP system for two tasks: evidence retrieval and natural language inference from clinical trial data. In this paper, we describe our two developed systems. The first one is a pipeline system that models the two tasks separately, while the second one is a joint system that learns the two tasks simultaneously with a shared representation and a multi-task learning approach. The final system combines their outputs in an ensemble system. We formalize the models, present their characteristics and challenges, and provide an analysis of achieved results. Our system ranked 3rd out of 40 participants with a final submission.
Non-Orthogonal Multiple Access For Near-Field Communications
Venue: arXiv cs.IT 2023
Year: 2023

The novel concept of near-field non-orthogonal multiple access (NF-NOMA) communications is proposed. The near-filed beamfocusing enables NOMA to be carried out in both angular and distance domains. Two novel frameworks are proposed, namely, single-location-beamfocusing NF-NOMA (SLB-NF-NOMA) and multiple-location-beamfocusing NF-NOMA (MLB-NF-NOMA). 1) For SLB-NF-NOMA, two NOMA users in the same angular direction with distinct quality of service (QoS) requirements can be grouped into one cluster. The hybrid beamformer design and power allocation problem is formulated to maximize the sum rate of the users with higher QoS (H-QoS) requirements. To solve this problem, the analog beamformer is first designed to focus the energy on the H-QoS users and the zero-forcing (ZF) digital beamformer is employed. Then, the optimal power allocation is obtained. 2) For MLB-NF-NOMA, the two NOMA users in the same cluster can have different angular directions. The analog beamformer is first designed to focus the energy on both two NOMA users. Then, a singular value decomposition (SVD) based ZF (SVD-ZF) digital beamformer is designed. Furthermore, a novel antenna allocation algorithm is proposed. Finally, a suboptimal power allocation algorithm is proposed. Numerical results demonstrate that the NF-NOMA can achieve a higher spectral efficiency and provide a higher flexibility than conventional far-field NOMA.
AI-assisted coding: Experiments with GPT-4
Venue: arXiv cs.AI 2023
Year: 2023

Artificial intelligence (AI) tools based on large language models have acheived human-level performance on some computer programming tasks. We report several experiments using GPT-4 to generate computer code. These experiments demonstrate that AI code generation using the current generation of tools, while powerful, requires substantial human validation to ensure accurate performance. We also demonstrate that GPT-4 refactoring of existing code can significantly improve that code along several established metrics for code quality, and we show that GPT-4 can generate tests with substantial coverage, but that many of the tests fail when applied to the associated code. These findings suggest that while AI coding tools are very powerful, they still require humans in the loop to ensure validity and accuracy of the results.
PROBE3.0: A Systematic Framework for Design-Technology Pathfinding with Improved Design Enablement
Venue: arXiv cs.AR 2023
Year: 2023

We propose a systematic framework to conduct design-technology pathfinding for PPAC in advanced nodes. Our goal is to provide configurable, scalable generation of process design kit (PDK) and standard-cell library, spanning key scaling boosters (backside PDN and buried power rail), to explore PPAC across given technology and design parameters. We build on PROBE2.0, which addressed only area and cost (AC), to include power and performance (PP) evaluations through automated generation of full design enablements. We also improve the use of artificial designs in the PPAC assessment of technology and design configurations. We generate more realistic artificial designs by applying a machine learning-based parameter tuning flow. We further employ clustering-based cell width-regularized placements at the core of routability assessment, enabling more realistic placement utilization and improved experimental efficiency. We demonstrate PPAC evaluation across scaling boosters and artificial designs in a predictive technology node.
Structure Diagram Recognition in Financial Announcements
Venue: arXiv cs.CV 2023
Year: 2023

Accurately extracting structured data from structure diagrams in financial announcements is of great practical importance for building financial knowledge graphs and further improving the efficiency of various financial applications. First, we proposed a new method for recognizing structure diagrams in financial announcements, which can better detect and extract different types of connecting lines, including straight lines, curves, and polylines of different orientations and angles. Second, we developed a two-stage method to efficiently generate the industry's first benchmark of structure diagrams from Chinese financial announcements, where a large number of diagrams were synthesized and annotated using an automated tool to train a preliminary recognition model with fairly good performance, and then a high-quality benchmark can be obtained by automatically annotating the real-world structure diagrams using the preliminary model and then making few manual corrections. Finally, we experimentally verified the significant performance advantage of our structure diagram recognition method over previous methods.
Analyzing In-browser Cryptojacking
Venue: arXiv cs.CR 2023
Year: 2023

Cryptojacking is the permissionless use of a target device to covertly mine cryptocurrencies. With cryptojacking, attackers use malicious JavaScript codes to force web browsers into solving proof-of-work puzzles, thus making money by exploiting the resources of the website visitors. To understand and counter such attacks, we systematically analyze the static, dynamic, and economic aspects of in-browser cryptojacking. For static analysis, we perform content, currency, and code-based categorization of cryptojacking samples to 1) measure their distribution across websites, 2) highlight their platform affinities, and 3) study their code complexities. We apply machine learning techniques to distinguish cryptojacking scripts from benign and malicious JavaScript samples with 100\% accuracy. For dynamic analysis, we analyze the effect of cryptojacking on critical system resources, such as CPU and battery usage. We also perform web browser fingerprinting to analyze the information exchange between the victim node and the dropzone cryptojacking server. We also build an analytical model to empirically evaluate the feasibility of cryptojacking as an alternative to online advertisement. Our results show a sizeable negative profit and loss gap, indicating that the model is economically infeasible. Finally, leveraging insights from our analyses, we build countermeasures for in-browser cryptojacking that improve the existing remedies.
The Closeness of In-Context Learning and Weight Shifting for Softmax Regression
Venue: arXiv cs.CL 2023
Year: 2023

Large language models (LLMs) are known for their exceptional performance in natural language processing, making them highly effective in many human life-related or even job-related tasks. The attention mechanism in the Transformer architecture is a critical component of LLMs, as it allows the model to selectively focus on specific input parts. The softmax unit, which is a key part of the attention mechanism, normalizes the attention scores. Hence, the performance of LLMs in various NLP tasks depends significantly on the crucial role played by the attention mechanism with the softmax unit. In-context learning, as one of the celebrated abilities of recent LLMs, is an important concept in querying LLMs such as ChatGPT. Without further parameter updates, Transformers can learn to predict based on few in-context examples. However, the reason why Transformers becomes in-context learners is not well understood. Recently, several works [ASA+22,GTLV22,ONR+22] have studied the in-context learning from a mathematical perspective based on a linear regression formulation $\min_x\| Ax - b \|_2$, which show Transformers' capability of learning linear functions in context. In this work, we study the in-context learning based on a softmax regression formulation $\min_{x} \| \langle \exp(Ax), {\bf 1}_n \rangle^{-1} \exp(Ax) - b \|_2$ of Transformer's attention mechanism. We show the upper bounds of the data transformations induced by a single self-attention layer and by gradient-descent on a $\ell_2$ regression loss for softmax prediction function, which imply that when training self-attention-only Transformers for fundamental regression tasks, the models learned by gradient-descent and Transformers show great similarity.
Simple Type Theory as a Clausal Theory
Venue: arXiv cs.LO 2023
Year: 2023

We give a presentation of Simple Type Theory as a clausal rewrite system in Polarized deduction modulo.
Specifying programs with propositions and with congruences
Venue: arXiv cs.LO 2023
Year: 2023

We give a presentation of Krivine and Parigot's Second-order functional arithmetic in Deduction modulo. Expressing this theory in Deduction modulo sheds light on an original aspect of this theory: the fact that programs are specified, not with propositions, but with congruences.
Blockchain-based Access Control for Secure Smart Industry Management Systems
Venue: arXiv cs.CR 2023
Year: 2023

Smart manufacturing systems involve a large number of interconnected devices resulting in massive data generation. Cloud computing technology has recently gained increasing attention in smart manufacturing systems for facilitating cost-effective service provisioning and massive data management. In a cloud-based manufacturing system, ensuring authorized access to the data is crucial. A cloud platform is operated under a single authority. Hence, a cloud platform is prone to a single point of failure and vulnerable to adversaries. An internal or external adversary can easily modify users' access to allow unauthorized users to access the data. This paper proposes a role-based access control to prevent modification attacks by leveraging blockchain and smart contracts in a cloud-based smart manufacturing system. The role-based access control is developed to determine users' roles and rights in smart contracts. The smart contracts are then deployed to the private blockchain network. We evaluate our solution by utilizing Ethereum private blockchain network to deploy the smart contract. The experimental results demonstrate the feasibility and evaluation of the proposed framework's performance.
STIR: Siamese Transformer for Image Retrieval Postprocessing
Venue: arXiv cs.IR 2023
Year: 2023

Current metric learning approaches for image retrieval are usually based on learning a space of informative latent representations where simple approaches such as the cosine distance will work well. Recent state of the art methods such as HypViT move to more complex embedding spaces that may yield better results but are harder to scale to production environments. In this work, we first construct a simpler model based on triplet loss with hard negatives mining that performs at the state of the art level but does not have these drawbacks. Second, we introduce a novel approach for image retrieval postprocessing called Siamese Transformer for Image Retrieval (STIR) that reranks several top outputs in a single forward pass. Unlike previously proposed Reranking Transformers, STIR does not rely on global/local feature extraction and directly compares a query image and a retrieved candidate on pixel level with the usage of attention mechanism. The resulting approach defines a new state of the art on standard image retrieval datasets: Stanford Online Products and DeepFashion In-shop. We also release the source code at https://github.com/OML-Team/open-metric-learning/tree/main/pipelines/postprocessing/ and an interactive demo of our approach at https://dapladoc-oml-postprocessing-demo-srcappmain-pfh2g0.streamlit.app/